Summarizer - Summarize any text

The Magic Explained - Summarizer

A brief background:
Born out of the University of Helsinki in Finland, Summarizer is the result of over 20 years worth of research and development in the areas of linguistics, IT and computer science by a company called Connexor.

The technology powering the application is comprised of a staggering 8 million lines of code and is used across the world by companies ranging from Microsoft to Motorola.

What problems does Summarizer solve?

Summarizer allows you to summarize long articles or reports in seconds, saving hours of precious time.

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Ever been in a situation where you have to read a huge amount of text but simply don’t have the time to get through it all? Perhaps you’re a student working on an assignment with a mountain of books and articles to get through, or a market research analyst with piles of reports to read just to get an overview. Well, that’s where Summarizer comes in!

How does Summarizer work?

Summarizer determines the most important parts of a document to form an accurate and meaningful summary in seconds using state-of-the-art text analysis methods.

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Connexor technology uses a range of text analyses, including morphological analysis, syntactic analysis, event detection, topic detection and sentiment detection. It also draws from a rich base of data resources, including dictionaries and ontologies.

These methods are combined to extract higher-level metadata that determine the main topic of the text. The application can then determine which pieces of text are the most relevant to that topic - and thus the most important to include in a summary.

What makes Summarizer special?

Summarizer combines Connexor’s complete range of syntactic analysis technologies to derive more accurate results, saving you significant amounts of time and resources and giving you results you can trust.

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Summarizing a piece of text isn’t easy. Language is often ambiguous. Take the following sentences for example:
“Time flies like an arrow”; and
“Fruit flies like banana”.

Knowing the difference between these two sentences requires in-depth knowledge of the world (in this case, that “time flies” don’t exist) in order to derive meaning.

There are rules in language, of course, but people often disobey them. And, as humans, we often make mistakes too, which may end up significantly altering the meaning of the text.

Whilst there are a number of text analysis solutions out there trying to tackle these issues, most of them combine low-end linguistic technologies with inaccurate statistical pattern matching techniques to discover relational information. The result is often inaccurate and not what the reader is looking for.